Deep Learning and GPU programming using OpenACC @ HLRS Stuttgart
Monday, July 15 - Wednesday, July 17, 2019, 9:00-17:00
HLRS, Room 0.438 / Rühle Saal, University of Stuttgart, Nobelstr. 19, D-70569 Stuttgart, Germany
NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.
Learn how to train and deploy a neural network to solve real-world problems, how to generate effective descriptions of content within images and video clips and how to accelerate your applications with OpenACC.
The workshop combines lectures about fundamentals of Deep Learning for Computer Vision and Multiple Data Types with a lecture about Accelerated Computing with OpenACC.
The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.
This workshop is co-organized by LRZ (Garching near Munich), HLRS (Stuttgart) and NVIDIA. All instructors are NVIDIA certified University Ambassadors.
1st day: Fundamentals of Deep Learning for Computer Vision
Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.
During this day, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:
Upon completion, you’ll be able to start solving problems on your own with deep learning.
2nd day: Fundamentals of Deep Learning for Multiple Data Types
This day explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.
Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:
Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.
3rd day: Guest Lecture & Fundamentals of Accelerated Computing with OpenACC
On the 3rd day we start with a guest lecture (9:00 - 10:30) about Deep Neural Networks for Data-Driven Turbulence Models, using DL in CFD:
Dr.-Ing. Andrea Beck, Institute of Aerodynamics and Gas Dynamics, University of Stuttgart. The abstract can be found here.
In the second part (10:45 - 17:00) you learn the basics of OpenACC, a high-level programming language for programming on GPUs. Discover how to accelerate the performance of your applications beyond the limits of CPU-only programming with simple pragmas. You’ll learn:
Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.
After you are accepted, please create an account under courses.nvidia.com/join using the same email address as for event registration, since lab access is given based on the event registration list. Please be aware that for adminstrative reasons, after you register, Nvidia will use your email address to contact you for the final feedback of the workshop.
The workshop is free of charge for all academic participants and coffee breaks will be provided (lunch is not included). Please note, that the workshop is exclusively for verifiable students, staff, and researchers from any academic institution (for industrial participants, please contact NVIDIA for industrial specific training). On the first day of the workshop, please bring your student/academia id.
NVIDIA Deep Learning Institute
The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.
Technical background, basic understanding of machine learning concepts, basic C/C++ or Fortran programming skills.
|DLI Instructors:||Yu Wang (LRZ), Volker Weinberg (LRZ), Momme Allalen (LRZ), Dr. -Ing. Andrea Beck (IAG), Dr. Khatuna Kakhiani (HLRS)
|Contact:||Dr. Volker Weinberg, email@example.com|